Autosomal dominant dopa-responsive parkinsonism in a multigenerational Swiss family.

AIM: To describe a large family with autosomal dominant parkinsonism. BACKGROUND: Seven genes are directly implicated in autosomally inherited parkinsonism. However, there are several multigenerational large families known with no identifiable mutation. MATERIAL AND METHODS: Family members were evaluated clinically, by history and chart review. Genetic investigation included SCA2, SCA3, UCHL1, SNCA, LRRK2, PINK1, PRKN, PGRN, FMR1 premutation, and MAPT. The proband underwent brain fluorodopa PET (FD-PET) scan, and one autopsy was available. RESULTS: Eleven patients had a diagnosis of Parkinson's disease (PD), nine women. Mean age of onset was 52 with tremor-predominant dopa-responsive parkinsonism. Disease progression was slow but severe motor fluctuations occurred. One patient required subthalamic nucleus deep-brain stimulation with a good motor outcome. One patient had mental retardation, schizophrenia and became demented, and another patient was demented. Three patients and also two unaffected subjects had mild learning difficulties. All genetic tests yielded negative results. FD-PET showed marked asymmetric striatal tracer uptake deficiency, consistent with PD. Pathological examination demonstrated no Lewy bodies and immunostaining was negative for alpha-synuclein. CONCLUSION: Apart from a younger age of onset and a female predominance, the phenotype was indistinguishable from sporadic tremor-predominant PD, including FD-PET scan results. As known genetic causes of autosomal dominant PD were excluded, this family harbors a novel genetic defect.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

DBS in Dystonia and Other Hyperkinetic Movement Disorders.

OPINION STATEMENT: The diagnosis and appropriate treatment of hyperkinetic movement disorders require a work up of potentially reversible metabolic, infectious and structural disorders as well as side effects of current medication. In pharmacoresistant movement disorders with a disabling impact on quality of life, deep brain stimulation (DBS) should be considered. At different targets, DBS has become an established therapy for Parkinson's disease (GPi-STN), tremor (VIM) and primary dystonia (GPi) with reasonable perioperative risks and side effects, established guidelines and some clinical and radiological predictive factors. In contrast, for other hyperkinetic movement disorders, including secondary dystonia, Gilles de la Tourette, chorea and ballism, only few data are available. Definite targets are not well defined, and reported results are of less magnitude than those of the recognized indications. In this expanding therapeutical field without worked out recommendations, an individual approach is needed with DBS indication assessment only after rigorous multidisciplinary scrutiny, restricted to expert centres.

Les effets de la maladie de Parkinson sur l'écriture manuscrite

Cet article présente les caractéristiques de l'écriture d'une personne atteinte de la maladie de Parkinson, ainsi que l'influence des traitements de la maladie sur ces caractéristiques. L'expert est ainsi amené à mieux connaître cette source de variation de l'écriture, et vise à l'aider à évaluer les résultats de ses observations lorsqu'il fait face à un cas impliquant des écritures de parkinsoniens. Puisque les écritures imitées peuvent présenter des caractéristiques similaires à celles des écritures pathologiques, il est important de connaître les signes qui permettent de les distinguer. L'écriture des parkinsoniens peut notamment présenter une diminution de la taille des lettres, connue sous le nom de "micrographie", des tremblements ainsi que des formes indistinctes des lettres, et un alignement ondulatoire des mots. Les parkinsoniens écrivent moins vite et avec une accélération et une fluidité réduites. Ils ont tendance à augmenter la durée de l'exécution de leurs traits. Le traitement au moyen de médicaments ou par Deep Brain Stimulation (DBS) peut permettre aux parkinsoniens d'améliorer leur performance et d'atteindre une restauration partielle de l'exécution des mouvements séquentiels.

Neurologie [Neurology].

In 2015, cerebral stimulation becomes increasingly established in the treatment of pharmacoresistant epilepsy. Efficacy of endovascular treatment has been demonstrated for acute ischemic stroke. Deep brain stimulation at low frequency improves dysphagia and freezing of gait in Parkinson patients. Bimagrumab seems to increase muscular volume and force in patients with inclusion body myositis. In cluster-type headache, a transcutaneous vagal nerve stimulator is efficient in stopping acute attacks and also reducing their frequency. Initial steps have been undertaken towards modulating memory by stimulation of the proximal fornix. Teriflunomide is the first oral immunomodulatory drug for which efficacy has been shown in preventing conversion from clinical isolated syndrome to multiple sclerosis.

Contribution of subcortical structures to cognition assessed with invasive electrophysiology in humans

Implantation of deep brain stimulation (DBS) electrodes via stereotactic neurosurgery has become a standard procedure for the treatment of Parkinson's disease. More recently, the range of neuropsychiatric conditions and the possible target structures suitable for DBS have greatly increased. The former include obsessive compulsive disease, depression, obesity, tremor, dystonia, Tourette's syndrome and cluster-headache. In this article we argue that several of the target structures for DBS (nucleus accumbens, posterior inferior hypothalamus, nucleus subthalamicus, nuclei in the thalamus, globus pallidus internus, nucleus pedunculopontinus) are located at strategic positions within brain circuits related to motivational behaviors, learning, and motor regulation. Recording from DBS electrodes either during the operation or post-operatively from externalized leads while the patient is performing cognitive tasks tapping the functions of the respective circuits provides a new window on the brain mechanisms underlying these functions. This is exemplified by a study of a patient suffering from obsessive-compulsive disease from whom we recorded in a flanker task designed to assess action monitoring processes while he received a DBS electrode in the right nucleus accumbens. Clear error-related modulations were obtained from the target structure, demonstrating a role of the nucleus accumbens in action monitoring. Based on recent conceptualizations of several different functional loops and on neuroimaging results we suggest further lines of research using this new window on brain functions.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Features for detection of Parkinson's disease tremor from local field potentials of the subthalamic nucleus

Deep Brain Stimulation (DBS) is a treatment routinely used to alleviate the symptoms of Parkinson's disease (PD). In this type of treatment, electrical pulses are applied through electrodes implanted into the basal ganglia of the patient. As the symptoms are not permanent in most patients, it is desirable to develop an on-demand stimulator, applying pulses only when onset of the symptoms is detected. This study evaluates a feature set created for the detection of tremor - a cardinal symptom of PD. The designed feature set was based on standard signal features and researched properties of the electrical signals recorded from subthalamic nucleus (STN) within the basal ganglia, which together included temporal, spectral, statistical, autocorrelation and fractal properties. The most characterized tremor related features were selected using statistical testing and backward algorithms then used for classification on unseen patient signals. The spectral features were among the most efficient at detecting tremor, notably spectral bands 3.5-5.5 Hz and 0-1 Hz proved to be highly significant. The classification results for determination of tremor achieved 94% sensitivity with specificity equaling one.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Attacking human implants: a new generation of cybercrime

Human ICT implants, such as RFID implants, cochlear implants, cardiac pacemakers, Deep Brain Stimulation, bionic limbs connected to the nervous system, and networked cognitive prostheses, are becoming increasingly complex. With ever-growing data processing functionalities in these implants, privacy and security become vital concerns. Electronic attacks on human ICT implants can cause significant harm, both to implant subjects and to their environment. This paper explores the vulnerabilities which human implants pose to crime victimisation in light of recent technological developments, and analyses how the law can deal with emerging challenges of what may well become the next generation of cybercrime: attacks targeted at technology implanted in the human body. After a state-of-the-art description of relevant types of human implants and a discussion how these implants challenge existing perceptions of the human body, we describe how various modes of attacks, such as sniffing, hacking, data interference, and denial of service, can be committed against implants. Subsequently, we analyse how these attacks can be assessed under current substantive and procedural criminal law, drawing on examples from UK and Dutch law. The possibilities and limitations of cybercrime provisions (eg, unlawful access, system interference) and bodily integrity provisions (eg, battery, assault, causing bodily harm) to deal with human-implant attacks are analysed. Based on this assessment, the paper concludes that attacks on human implants are not only a new generation in the evolution of cybercrime, but also raise fundamental questions on how criminal law conceives of attacks. Traditional distinctions between physical and non-physical modes of attack, between human bodies and things, between exterior and interior of the body need to be re-interpreted in light of developments in human implants. As the human body and technology become increasingly intertwined, cybercrime legislation and body-integrity crime legislation will also become intertwined, posing a new puzzle that legislators and practitioners will sooner or later have to solve.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Limiting glutamate transmission in a Vglut2-expressing subpopulation of the subthalamic nucleus is sufficient to cause hyperlocomotion

The subthalamic nucleus (STN) is a key area of the basal ganglia circuitry regulating movement. We identified a subpopulation of neurons within this structure that coexpresses Vglut2 and Pitx2, and by conditional targeting of this subpopulation we reduced Vglut2 expression levels in the STN by 40%, leaving Pitx2 expression intact. This reduction diminished, yet did not eliminate, glutamatergic transmission in the substantia nigra pars reticulata and entopeduncular nucleus, two major targets of the STN. The knock-out mice displayed hyperlocomotion and decreased latency in the initiation of movement while preserving normal gait and balance. Spatial cognition, social function, and level of impulsive choice also remained undisturbed. Furthermore, these mice showed reduced dopamine transporter binding and slower dopamine clearance in vivo, suggesting that Vglut2-expressing cells in the STN regulate dopaminergic transmission. Our results demonstrate that altering the contribution of a limited population within the STN is sufficient to achieve results similar to STN lesions and high-frequency stimulation, but with fewer side effects.

Interferindo com oscilações de alta frequência no hipocampo epiléptico: consequências para as crises espontâneas

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Neurogenic neuroprotection: Future perspectives

Neurogenic neuroprotection elicited by deep brain stimulation is emerging as a promising approach for treating patients with ischemic brain lesions. In rats, stimulation of the fastigial nucleus, but not dentate nucleus, has been shown to reduce the volume of focal infarction. Protection of neural tissue is a rapid intervention that has a relatively long-lasting effect, rendering fastigial nucleus stimulation (FNS) a potentially valuable method for clinical application. We review some of the main findings of animal experimental research from a clinical perspective. Results: Although the complete mechanisms of neuroprotection induced by FNS remain unclear, important data has been presented in the last two decades. The acute effect of electrical stimulation of the fastigial nucleus is likely mediated by a prolonged opening of potassium channels, and the sustained effect appears to be linked to inhibition of the apoptotic cascade. A better understanding of the cellular and molecular mechanisms underlying neurogenic neuroprotection by stimulation of deep brain nuclei, with special attention to the fastigial nucleus, can contribute toward improving neurological outcomes in ischemic brain insults.